Feature: Consider SenseVoice for training data transcription

Hi! F5-TTS is great work on flow-matching based speech synthesis. For training data transcription and annotation, [SenseVoice](https://github.com/FunAudioLLM/SenseVoice) could be useful: **Why SenseVoice for TTS data preparation:** - **5x faster** than Whisper large-v3 (234M params, non-autoregressive) - **Emotion detection** — classify utterance emotion for expressive TTS training - **Audio event detection** — filter out noisy/contaminated audio samples - **50+ languages** — multilingual data annotation - Word-level timestamps available via [OmniSenseVoice](https://github.com/lifeiteng/OmniSenseVoice) ```python from funasr import AutoModel model = AutoModel(model="iic/SenseVoiceSmall") result = model.generate(input="audio.wav") # Text + emotion + audio events in one pass ``` GPT-SoVITS (58K stars) already uses FunASR for their training data annotation pipeline with good results. - FunASR: https://github.com/modelscope/FunASR (16K+ stars) - SenseVoice: https://github.com/FunAudioLLM/SenseVoice (8K+ stars)